Statistical Foundations of Actuarial Learning and its Applications
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Beschreibung
This open access book discusses the statistical modeling of insurance problems, a process which comprises data collection, data analysis and statistical model building to forecast insured events that may happen in the future. It presents the mathematical foundations behind these fundamental statistical concepts and how they can be applied in daily actuarial practice.
Statistical modeling has a wide range of applications, and, depending on the application, the theoretical aspects may be weighted differently: here the main focus is on prediction rather than explanation. Starting with a presentation of state-of-the-art actuarial models, such as generalized linear models, the book then dives into modern machine learning tools such as neural networks and text recognition to improve predictive modeling with complex features.
Providing practitioners with detailed guidance on how to apply machine learning methods to real-world data sets, and how tointerpret the results without losing sight of the mathematical assumptions on which these methods are based, the book can serve as a modern basis for an actuarial education syllabus.
This open access book discusses the statistical modeling of insurance problems, a process which comprises data collection, data analysis and statistical model building to forecast insured events that may happen in the future. It presents the mathematical foundations behind these fundamental statistical concepts and how they can be applied in daily actuarial practice.
Statistical modeling has a wide range of applications, and, depending on the application, the theoretical aspects may be weighted differently: here the main focus is on prediction rather than explanation. Starting with a presentation of state-of-the-art actuarial models, such as generalized linear models, the book then dives into modern machine learning tools such as neural networks and text recognition to improve predictive modeling with complex features.
Providing practitioners with detailed guidance on how to apply machine learning methods to real-world data sets, and how to interpret the results without losing sight of the mathematical assumptions on which these methods are based, the book can serve as a modern basis for an actuarial education syllabus.
This book is open access, which means that you have free and unlimited access Uniquely combines classical statistical modeling with modern machine learning methods Discusses the state-of-the-art in predictive modeling for actuarial work Provides machine learning and statistical tools with a focus on actuarial applications and their special features
Autor*in
Mario V. Wüthrich
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Open Access Deep Learning Actuarial Modeling Pricing and Claims Reserving Artificial Neural Networks Regression Modeling
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“I enjoyed reading this book. The mathematical methods presented are precisely suited to analyzing questions, solving problems, and drawing meaningful conclusions. The authors provide recommendations for further reading, allowing readers to deepen their knowledge if needed. The clarity of the exposition, combined with the interplay between statistical and machine learning approaches to model selection, estimation, and prediction, make this book an outstanding resource. I highly recommend it to both researchers and practitioners in statistical modeling.” (Olivier Menoukeu Pamen, SIAM Review, Issue 3, September, 2025)
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